Collaborative filtering recommendation algorithm based on user ratings and item attributes

Jiaming Zhang, Kaoru Hirota*, Yaping Dai, Zheng Meng

*此作品的通讯作者

科研成果: 会议稿件论文同行评审

摘要

A collaborative filtering recommendation algorithm is proposed in recommender systems based on user ratings and item attributes to relieve the problem of data sparsity, which introduces the information of item attributes and considers the similarity of item attributes preference of users as well as the similarity of user ratings. It improves the accuracy of recommendation under the influence of data sparsity compared to the traditional collaborative filtering (CF) algorithm considering user ratings only. The experimental results of the proposed algorithm based on MovieLens data set are compared with the traditional CF algorithm and it is found that the mean absolute error (MAE) reduces by 0.08. The proposal improves the recommendation accuracy in recommendation of the commodity and social network and gives more satisfactory recommendation to users.

源语言英语
出版状态已出版 - 2017
活动5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017 - Beijing, 中国
期限: 2 11月 20175 11月 2017

会议

会议5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017
国家/地区中国
Beijing
时期2/11/175/11/17

指纹

探究 'Collaborative filtering recommendation algorithm based on user ratings and item attributes' 的科研主题。它们共同构成独一无二的指纹。

引用此